Understanding people's
perception of their
carbon footprint

For the climate, flying to New York is worse than taking a long shower. But is it 10 times worse or is it 1000 times worse? The carbon footprint of our actions have been widely analyzed and quantified, but it does not mean that people are well aware of their impact.

With this project, we would like to understand people's perception of their carbon footprint, and how does it compare to the actual carbon footprint of actions they take. This could help climate scientists, sociologists, news outlets, politicians, and the general public to improve climate communication and enhance climate action.

In an ideal world, we would ask people to answer questions such as "How much CO2 does taking the plane emit?", for all actions. But this is a tedious, difficult task for non-experts. Hence, we propose to compare pairs of actions. It is probably easier to say "Flying emits 1,000 times more CO2 than taking a shower".

To tackle this problem, we use recent advances in machine learning to run Climpact. To estimate the actual impact of actions from pairwise comparisons, we developed a statistical model of the observed impact ratio (i.e., "1,000 times more" from the flying and showering example above). This model enables us to:

  1. compute implicitly the carbon footprint of all actions;
  2. measure the uncertainty inherent in the answers we collect; and
  3. select the best pair of actions to show in order to obtain meaningful data.


We computed the carbon footprint of 52 actions from scratch using a life-cycle analysis methodology (ISO 14040). We will make this rich dataset publicly available on the platform through an interactive interface. Stay tuned!


Climpact is an original idea from Victor Kristof and Lucas Maystre in Patrick Thiran's and Matthias Grossglauser's INDY Lab at EPFL. The life-cycle analysis and the creation of the dataset is the result of an academic project by Alexis Barrou, Edouard Cattin, and Blanche Dalimier, under the supervision of Jérôme Payet. This work has won EPFL's Durabilis Award 2021.

Want to know more? Shoot us an email.